We use CIFAR-10 [1]}, CIFAR-100 [1]}, STL-10 [3]}, Tiny-Imagenet-200 [4]} and Imagenet-1k [5]} data sets, covering a wide range of variability in number of training samples, number of classes and image resolutions. CIFAR-10 has 10 classes with 5,000 32x32 resolution images per class, whereas CIFAR-100 has the same number of total images but among 100 classes, constraining the training set to only 500 32x32 images per class. STL-10 has 10 classes with 500 training and 800 test images per class which are labelled but these images are of higher resolution (96x96) than CIFAR. Tiny-Imagenet-200 has 200 classes with 100,000 labelled training images of 64x64 resolution. Imagenet-1k has 1.2 million images with varying resolutions from 1,000 classes.
